Conferences - Seminars

Machine-learning of density functionals for applications in molecules and materials

By Prof. Kieron Burke (University of California, Irvine)

This lecture is designed to be accessible to a wide variety of backgrounds.
In the first part, I will briefly review density functional theory and why it is important to many branches of modern physical science. I will also review machine learning and its recent applications to molecules and materials.
In the second half, I will show how, in collaboration with computer scientists at TU Berlin, we have used a specific type of machine-learning, called kernel ridge regression, to find more accurate and powerful approximate density functionals than any made by humans.
For more background, see here.